Category: Blog

There’s an argument over at Andrew Gelman’s blog about the proper way to design a variance prior in a hierarchical normal model (here and here). Since this is more or less my go-to approach to meta-analysis (e.g. Karr, … Read more

The failure of scientists, and particularly students in the sciences, to properly understand the most commonly used statistical concepts in their field has been extensively documented (see e.g. Sotos et al., 2007). Many of the specific misunderstandings are well known … Read more

In preparation for a huge fMRI dataset — which we’re nearly finished collecting — I’ve been trying to set up some kind of sensible pipeline for doing general statistical modeling / machine learning on functional brain network data. This is … Read more

I recently came across an exchange in Psyc. Science that perfectly illustrates some of the problems involved with the use of Bayes factors. Scheibehenne, Jamil, and Wagenmakers (2016a) meta-analyze the probability of hotel towel reuse in two conditions, and compare … Read more

Extending on my previousmost-reported-summary-statistics-for-factor-analysis-favor-overfitting comments, which focused on the common summary statistics used to evaluate factor models, I thought it might be interesting to tackle the other side of the problem, which is that covariance estimates tend to be … Read more

I mentioned earlier that the whole “Bayesian inference is immune to stopping rules” excitement is largely overblown, but the simulations were based on pretty idealized situations. I thought it might be interesting to use summary statistics reported by an actual … Read more

I don’t know anything about cellular neuroscience or single cell recording, but I recently came across the problem of estimating the receptive field of a neuron from its spiking frequency in response to movement at various angles. This is directional … Read more

This post describes step 1 of my quest to build a fully Bayesian general linear model for functional data. I haven’t done it yet, and any solution is likely to be very computationally expensive, but so far I’ve had a … Read more

As part of my playing around with alternative objective functions for estimating reinforcement learning models of the Iowa Gambling Task (IGT), I needed a way to quickly simulate large numbers of participants. Since base R is slow, I implemented a … Read more